Optimizing Battery Storage Trading and Valuation in Intraday Electricity Markets

Summary

The paper presents a method for operating and valuing electricity storage when intraday prices are uncertain. It formulates the charging and discharging problem as convex stochastic optimization and uses Stochastic Dual Dynamic Programming to solve it. The setup accounts for a finite set of delivery periods, bid-ask spreads, and physical restrictions such as storage capacity and charging speed.

Storage value is assessed using indifference pricing: the valuation reflects the agent’s financial position, market views, and risk preferences through the optimal trading strategy. The authors report that the approach can find strategies in minutes on a regular computer, and that both recommended operation and battery valuation change consistently with risk preferences and battery characteristics. The supplied description gives no numerical performance comparison or market dataset, so it does not establish how the method performs against alternative trading or valuation approaches in live markets.

Key ideas

• The storage problem is framed as convex stochastic optimization under uncertain electricity prices.
• Stochastic Dual Dynamic Programming is used to optimize charging and discharging across delivery periods.
• The model can include capacity and charging-speed constraints as well as bid-ask spreads.
• Indifference pricing ties battery value to the agent’s position, market views, and risk preferences.
• The reported optimal strategies and valuations vary with both risk preferences and battery characteristics.

Tags

# Optimal Operation and Valuation of Electricity Storages in Intraday Markets


# Optimal Operation and Valuation of Electricity Storages in Intraday Markets









This paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's financial position, market views and risk preferences. The underlying optimization problem is solved with the Stochastic Dual Dynamic Programming algorithm which is applicable to various specifications of storages, and it allows for e.g. hard constraints on storage capacity and charging speed. We illustrate the approach in intraday trading where the agent charges or discharges a battery over a finite number of delivery periods, and the electricity prices are subject to bid-ask spreads and significant uncertainty. Optimal strategies are found in a matter of minutes on a regular PC. We find that the corresponding trading strategies and battery valuations vary consistently with respect to the agent's risk preferences as well as the physical characteristics of the battery.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.